298 research outputs found

    Multi-Agent Systems

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    A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

    Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

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    The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Path Building in Emerging Entrepreneurial Firms: An Investigation of Networks in the Making

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    Underpinning economic growth is the emergence of entrepreneurial ventures with the potential to grow that boost job creation and provide new sources of products for mature companies. The critical role associated with new firms, underscores the importance of understanding how entrepreneurship unfolds. Network-based research, while leading the way to rich empirical studies provides a limited understanding of how entrepreneurial networks are built and their impact on the emergence of a new venture. Employing a multiple case study design and a perspective based on organizational path building, three young technology ventures were investigated in terms of the formation of networks around five key entrepreneurial activities defined by entrepreneurs. Rich insight into new venture emergence is presented in terms of the reciprocal relationship between specific activities enacted by entrepreneurs and the networks that form to execute those activities revealing the path building mechanisms that evolve to drive network development. The findings of this research not only contribute to theories of new venture emergence, but also offer an interesting opportunity for future research into factors that may influence the outcome of entrepreneurial ventures and provide practical insight for organizations seeking to sustain or develop an entrepreneurial path

    Being the Bottom Line: Mothers' Experiences of Fostering Networks That Will Support Their Son or Daughter with Disabilities

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    This phenomenological study was part of a wider ethnographic research project of nine personal support networks. Participants were purposefully recruited to the project because of their involvement in networks that were committed to actively developing the positive, meaningful future of an adult family member with lifelong disabilities. Data were collected from November 2007 to March 2012. A narrative analysis of a subset of the data, the transcripts of interviews with the mother of the son or daughter with disabilities at the centre of eight of the networks, was conducted for the purposes of this study. Findings were checked with mothers. The mothers in this study anticipated the family, particularly sisters, circles, the service and/ or a good village will support their son or daughter with disabilities when they, and the father of their son or daughter, are no longer able to. They did so by embodying appropriate ways of thinking, supporting siblings and, meeting the shortfall. The networks they anticipated for the future were uniquely configured however they were influenced by transitions and turning points in the lives of their son or daughter with lifelong disabilities. Mothers were the bottom line in these networks. Paradoxically, they exercised their sense of responsibility by engaging others in the networks that will support their sons and daughters into the future. The findings from this study will inform the further development of initiatives that support parents in the process of preparing for the future support of their son or daughter with lifelong disabilities

    High-Performance Modelling and Simulation for Big Data Applications

    Get PDF
    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Design for manufacturability : a feature-based agent-driven approach

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    Agent-oriented constructivist knowledge management

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    In Ancient Times, when written language was introduced, books and manuscripts were often considered sacred. During these times, only a few persons were able to read and interpret them, while most people were limited in accepting these interpretations. Then, along with the industrial revolution of the XVIII and XIX centuries and especially boosted by the development of the press, knowledge slowly became available to all people. Simultaneously, people were starting to apply machines in the development of their work, usually characterized by repetitive processes, and especially focused in the production of consuming goods, such as furniture, clocks, clothes and so on. Following the needs of this new society, it was finally through science that new processes emerged to enable the transmission of knowledge from books and instructors to learners. Still today, people gain knowledge based on these processes, created to fulfill the needs of a society in its early stages of industrialization, thus not being compatible with the needs of the information society. In the information society, people must deal with an overloading amount of information, by the means of the media, books, besides different telecommunication and information systems technology. Furthermore, people’s relation to work has been influenced by profound changes, for instance, knowledge itself is now regarded as a valuable work product and, thus, the workplace has become an environment of knowledge creation and learning. Modifications in the world economical, political and social scenarios led to the conclusion that knowledge is the differential that can lead to innovation and, consequently, save organizations, societies, and even countries from failing in achieving their main goals. Focusing on these matters is the Knowledge Management (KM) research area, which deals with the creation, integration and use of knowledge, aiming at improving the performance of individuals and organizations. Advances in this field are mainly motivated by the assumption that organizations should focus on knowledge assets (generally maintained by the members of an organization) to remain competitive in the information society’s market. This thesis argues that KM initiatives should be targeted based on a constructivist perspective. In general, a constructivist view on KM focuses on how knowledge emerges, giving great importance to the knowledge holders and their natural practices. With the paragraph above, the reader may already have an intuition of how this work faces and targets Knowledge Management, however, let us be more precise. Research in Knowledge Management has evolved substantially in the past 30 years, coming from a centralized view of KM processes to a distributed view, grounded in organizational and cognitive sciences studies that point out the social, distributed, and subjective nature of knowledge. The first Knowledge Management Systems (KMSs) were centrally based and followed a top-down design approach. The organization managers, supported by knowledge engineers, collected and structured the contents of an organizational memory as a finished product at design time (before the organizational memory was deployed) and then disseminated the product, expecting employees to use it and update it. However, employees often claimed that the knowledge stored in the repository was detached from their real working practices. This led to the development of evolutionary methods, which prescribe that the basic KM system is initially developed and evolves proactively in an on-going fashion. However, most of the initiatives are still based on building central repositories and portals, which assume standardized vocabularies, languages, and classification schemes. Consequently, employees’ lack of trust and motivation often lead to dissatisfaction. In other words, workers resist on sharing knowledge, since they do not know who is going to access it and what is going to be done with it. Moreover, the importance attributed to knowledge may give an impression that these central systems take away a valuable asset from his or her owner, without giving appreciable benefits in return. The problems highlighted in the previous paragraph may be attenuated or even solved if a top-down/bottom-up strategy is applied when proposing a KM solution. This means that the solution should be sought with aim at organizational goals (top-down) but at the same time, more attention should be given to the knowledge holders and on the natural processes they already use to share knowledge (bottom-up). Being active agency such an important principle of Constructivism, this work recognizes that the Agent Paradigm (first defined by Artificial Intelligence and more recently adopted by Software Engineering) is the best approach to target Knowledge Management, taking a technological and social perspective. Capable of modeling and supporting social environments, agents is here recognized as a suitable solution for Knowledge Management especially by providing a suitable metaphor used for modeling KM domains (i.e. representing humans and organizations) and systems. Applying agents as metaphors on KM is mainly motivated by the definition of agents as cognitive beings having characteristics that resemble human cognition, such as autonomy, reactivity, goals, beliefs, desires, and social-ability. Using agents as human abstractions is motivated by the fact that, for specific problems, such as software engineering and knowledge management process modeling, agents may aid the analyst to abstract away from some of the problems related to human complexity, and focus on the important issues that impact the specific goals, beliefs and tasks of agents of the domain. This often leads to a clear understanding of the current situation, which is essential for the proposal of an appropriate solution. The current situation may be understood by modeling at the same time the overall goals of the organization, and the needs and wants of knowledge holders. Towards facilitating the analysis of KM scenarios and the development of adequate solutions, this work proposes ARKnowD (Agent-oriented Recipe for Knowledge Management Systems Development). Systems here have a broad definition, comprehending both technology-based systems (e.g. information system, groupware, repositories) and/or human systems, i.e. human processes supporting KM using non-computational artifacts (e.g. brain stormings, creativity workshops). The basic philosophical assumptions behind ARKnowD are: a) the interactions between human and system should be understood according to the constructivist principle of self-construction, claiming that humans and communities are self-organizing entities that constantly construct their identities and evolve throughout endless interaction cycles. As a result of such interactions, humans shape systems and, at the same time, systems constrain the ways humans act and change; b) KM enabling systems should be built in a bottom-up approach, aiming at the organizational goals, but understanding that in order to fulfill these goals, some personal needs and wants of the knowledge holders (i.e. the organizational members) need to be targeted; and c) there is no “silver bullet��? when pursuing a KM tailoring methodology and the best approach is combining existing agent-oriented approaches according to the given domain or situation. This work shows how the principles above may be achieved by the integration of two existing work on agent-oriented software engineering, which are combined to guide KM analysts and system developers when conceiving KM solutions. Innovation in our work is achieved by supporting topdown/bottom-up approaches to KM as mentioned above. The proposed methodology does that by strongly emphasizing the earlier phases of software development, the so-called requirement analysis activity. In this way, we consider all stakeholders (organizations and humans) as agents in our analysis model, and start by understanding their relations before actually thinking of developing a system. Perhaps the problem may be more effectively solved by proposing changes in the business processes, rather than by making use of new technology. And besides, in addition to humans and organizations, existing systems are also included in the model from start, helping the analyst and designer to understand which functionalities are delegated to these so-called artificial agents. In addition to that, benefits as a result of the application of ARKnowD may be also attributed to our choice of using the proper agent cognitive characteristics in the different phases of the development cycle. With the main purpose of exemplifying the use of the proposed methodology, this work presents a socially-aware recommender agent named KARe (Knowledgeable Agent for Recommendations). Recommender Systems may be defined by those that support users in selecting items of their need from a big set of items, helping users to overcome the overwhelming feeling when facing a vast information source, such as the web, an organizational repository or the like. Besides serving as a case for our methodology, this work also aims at exploring the suitability of the KARe system to support KM processes. Our choice for supporting knowledge sharing through questioning and answering processes is again supported by Constructivism proponents, who understand that social interaction is vital for active knowledge building. This assumption is also defended by some KM theories, claiming that knowledge is created through cycles of transformation between two types of knowledge: tacit and explicit knowledge. Up to now, research on KM has paid much attention to the formalization and exchange of explicit knowledge, in the form of documents or other physical artifacts, often annotated with metadata, and classified by taxonomies or ontologies. Investigations surrounding tacit knowledge have been so far scarce, perhaps by the complexity of the tasks of capturing and integrating such kind of knowledge, defined as knowledge about personal experience and values, usually confined on people’s mind. Taking a flexible approach on supporting this kind of knowledge conversion, KARe relies on the potential of social interaction underlying organizational practices to support knowledge creation and sharing. The global objective of this work is to support knowledge creation and sharing within an organization, according to its own natural processes and social behaviors. In other words, this work is based on the assumption that KM is better supported if knowledge is looked at from a constructivist perspective. To sum up, this thesis aims at: 1) Providing an agent-oriented approach to guide the creation and evolvement of KM initiatives, by analyzing the organizational potentials, behaviors and processes concerning knowledge sharing; 2) Developing the KARe recommender system, based on a semantically enriched Information Retrieval technique for recommending knowledge artifacts, supporting users to ask and answer to each others’ questions. These objectives are achieved as follows: - Defining the principles that characterize a Constructivist KM supporting environment and understanding how they may be used to support the creation of more effective KM solutions; - Providing an agent-oriented approach to develop KM systems. This approach is based on the integration of two different agent-oriented software engineering works, profiting from their strengths in providing a comprehensive methodology that targets both analysis and design activities; - Proposing and designing a socially aware agent-oriented recommender system both to exemplify the application of the proposed approach and to explore its potential on supporting knowledge creation and sharing. - Implementing an Information Retrieval algorithm to support the previously mentioned system in generating recommendations. Besides describing the algorithm, this thesis brings experimental results to prove its effectiveness
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